Homework 12
Ketong Zhang
2024-03-04
Instructions
Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above. For any included plots, make sure they are clearly
labeled. You are free to use any plot type that you feel best
communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(tidyverse)
library(janitor)
library(ggmap)Load the Data
We will use two separate data sets for this homework.
The first data set represent sightings of grizzly bears (Ursos arctos) in Alaska.
The second data set is from Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
register_stadiamaps("311578bf-a7b3-4a46-955f-50b1b0fe5edc", write = FALSE)- Load the
grizzlydata and evaluate its structure.
grizzly <- read_csv("data/bear-sightings.csv")%>% clean_names()## Rows: 494 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): bear.id, longitude, latitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.summary(grizzly)## bear_id longitude latitude
## Min. : 7 Min. :-166.2 Min. :55.02
## 1st Qu.:2569 1st Qu.:-154.2 1st Qu.:58.13
## Median :4822 Median :-151.0 Median :60.97
## Mean :4935 Mean :-149.1 Mean :61.41
## 3rd Qu.:7387 3rd Qu.:-145.6 3rd Qu.:64.13
## Max. :9996 Max. :-131.3 Max. :70.37- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
grizzly %>%
select(latitude, longitude) %>%
summary()## latitude longitude
## Min. :55.02 Min. :-166.2
## 1st Qu.:58.13 1st Qu.:-154.2
## Median :60.97 Median :-151.0
## Mean :61.41 Mean :-149.1
## 3rd Qu.:64.13 3rd Qu.:-145.6
## Max. :70.37 Max. :-131.3longitude <- c(-166.2, -131.3)
latitude <- c(55.02, 70.37)
gri_bbox <- make_bbox(longitude, latitude, f = 0.03)- Load a map from
stamenin a terrain style projection and display the map.
sta <- get_stadiamap(gri_bbox,maptype = "stamen_terrain", zoom=7)## ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.## ℹ 196 tiles needed, this may take a while (try a smaller zoom?)ggmap(sta)
- Build a final map that overlays the recorded observations of grizzly bears in Alaska.
ggmap(sta)+
geom_point(data=grizzly, aes(x=longitude, y=latitude), size=0.8)+
labs(x="Longitude", y="Latitude", title="Observations of grizzly bears in Alaska")
Let’s switch to the wolves data. Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
- Load the data and evaluate its structure.
wolves <- read.csv("data/wolves_data/wolves_dataset.csv") %>% clean_names()summary(wolves)## pop year age_cat sex
## Length:1986 Min. :1992 Length:1986 Length:1986
## Class :character 1st Qu.:2006 Class :character Class :character
## Mode :character Median :2011 Mode :character Mode :character
## Mean :2010
## 3rd Qu.:2016
## Max. :2019
##
## color lat long habitat
## Length:1986 Min. :33.89 Min. :-157.84 Min. : 254.1
## Class :character 1st Qu.:44.60 1st Qu.:-123.73 1st Qu.:10375.2
## Mode :character Median :46.83 Median :-110.99 Median :11211.3
## Mean :50.43 Mean :-116.86 Mean :12797.4
## 3rd Qu.:57.89 3rd Qu.:-110.55 3rd Qu.:11860.8
## Max. :80.50 Max. : -82.42 Max. :34676.6
##
## human pop_density pack_size standard_habitat
## Min. : 0.02 Min. : 3.74 Min. :3.55 Min. :-1.63390
## 1st Qu.: 80.60 1st Qu.: 7.40 1st Qu.:5.62 1st Qu.:-0.30620
## Median :2787.67 Median :11.63 Median :6.37 Median :-0.19650
## Mean :2335.38 Mean :14.91 Mean :6.47 Mean : 0.01158
## 3rd Qu.:3973.47 3rd Qu.:25.32 3rd Qu.:8.25 3rd Qu.:-0.11130
## Max. :6228.64 Max. :33.96 Max. :9.56 Max. : 2.88180
##
## standard_human standard_pop standard_packsize standard_latitude
## Min. :-0.9834 Min. :-1.13460 Min. :-1.7585 Min. :-1.805900
## 1st Qu.:-0.9444 1st Qu.:-0.74630 1st Qu.:-0.5418 1st Qu.:-0.636900
## Median : 0.3648 Median :-0.29760 Median :-0.1009 Median :-0.392600
## Mean : 0.1461 Mean : 0.05084 Mean :-0.0422 Mean :-0.000006
## 3rd Qu.: 0.9383 3rd Qu.: 1.15480 3rd Qu.: 1.0041 3rd Qu.: 0.814300
## Max. : 2.0290 Max. : 2.07150 Max. : 1.7742 Max. : 3.281900
##
## standard_longitude cav_binary cdv_binary cpv_binary
## Min. :-2.144100 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:-0.359500 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median : 0.306900 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :-0.000005 Mean :0.8529 Mean :0.2219 Mean :0.7943
## 3rd Qu.: 0.330200 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. : 1.801500 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :321 NA's :21 NA's :7
## chv_binary neo_binary toxo_binary
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.8018 Mean :0.2804 Mean :0.4832
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :548 NA's :538 NA's :827- How many distinct wolf populations are included in this study? Make a new object that restricts the data to the wolf populations in the lower 48 US states.
n_distinct(wolves$pop)## [1] 17table(wolves$pop)##
## AK.PEN BAN.JAS BC DENALI ELLES GTNP INT.AK MEXICAN MI MT
## 100 96 145 154 11 60 35 181 102 351
## N.NWT ONT SE.AK SNF SS.NWT YNP YUCH
## 67 60 10 92 34 383 105new_wolves <- filter(wolves, pop %in% c("MT", "YNP", "GTNP", "MI"))
summary(new_wolves)## pop year age_cat sex
## Length:896 Min. :1997 Length:896 Length:896
## Class :character 1st Qu.:2006 Class :character Class :character
## Mode :character Median :2010 Mode :character Mode :character
## Mean :2009
## 3rd Qu.:2014
## Max. :2019
##
## color lat long habitat
## Length:896 Min. :43.82 Min. :-110.99 Min. : 9511
## Class :character 1st Qu.:44.60 1st Qu.:-110.99 1st Qu.:11166
## Mode :character Median :46.15 Median :-110.55 Median :11166
## Mean :45.60 Mean :-108.03 Mean :10944
## 3rd Qu.:46.83 3rd Qu.:-110.55 3rd Qu.:11211
## Max. :46.83 Max. : -86.82 Max. :11211
##
## human pop_density pack_size standard_habitat
## Min. :3240 Min. :11.63 Min. :5.620 Min. :-0.4196
## 1st Qu.:3240 1st Qu.:11.63 1st Qu.:5.620 1st Qu.:-0.2024
## Median :3973 Median :23.03 Median :7.120 Median :-0.2024
## Mean :3939 Mean :21.82 Mean :7.081 Mean :-0.2316
## 3rd Qu.:3973 3rd Qu.:28.93 3rd Qu.:8.250 3rd Qu.:-0.1965
## Max. :6229 Max. :33.96 Max. :8.250 Max. :-0.1965
##
## standard_human standard_pop standard_packsize standard_latitude
## Min. :0.5834 Min. :-0.2976 Min. :-0.5418 Min. :-0.7219
## 1st Qu.:0.5834 1st Qu.:-0.2976 1st Qu.:-0.5418 1st Qu.:-0.6369
## Median :0.9383 Median : 0.9119 Median : 0.3399 Median :-0.4677
## Mean :0.9218 Mean : 0.7833 Mean : 0.3170 Mean :-0.5276
## 3rd Qu.:0.9383 3rd Qu.: 1.5378 3rd Qu.: 1.0041 3rd Qu.:-0.3926
## Max. :2.0290 Max. : 2.0715 Max. : 1.0041 Max. :-0.3926
##
## standard_longitude cav_binary cdv_binary cpv_binary
## Min. :0.3069 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.3069 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.3302 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.4618 Mean :0.8326 Mean :0.2905 Mean :0.8881
## 3rd Qu.:0.3302 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.5716 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :6 NA's :1 NA's :2
## chv_binary neo_binary toxo_binary
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.7854 Mean :0.3692 Mean :0.4569
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :183 NA's :189 NA's :478- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
new_wolves %>%
select(long, lat) %>%
summary()## long lat
## Min. :-110.99 Min. :43.82
## 1st Qu.:-110.99 1st Qu.:44.60
## Median :-110.55 Median :46.15
## Mean :-108.03 Mean :45.60
## 3rd Qu.:-110.55 3rd Qu.:46.83
## Max. : -86.82 Max. :46.83long <- c(-110.99, -86.82)
lat <- c(43.82, 46.83)
wolf <- make_bbox(long, lat, f=0.05)- Load a map from
stamenin aterrain-linesprojection and display the map.
new_map <- get_stadiamap(wolf, maptype = "stamen_terrain_lines", zoom = 5)## ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.ggmap(new_map)
- Build a final map that overlays the recorded observations of wolves in the lower 48 states.
ggmap(new_map)+
geom_point(data=new_wolves, aes(x=long, y=lat), size=0.8)+
labs(x="Longitude", y="Latitude", title="Observations of wolves in lower 48 states")
- Use the map from #9 above, but add some aesthetics. Try to
fillandcolorby population.
ggmap(new_map)+
geom_point(data=new_wolves, aes(x=long, y=lat, color = pop), size=0.8)+
labs(x="Longitude", y="Latitude", title="Observations of wolves in lower 48 states")
Push your final code to GitHub!
Please be sure that you check the keep md file in the
knit preferences.